Modernizing Data Practices to Improve Government Act

• Authority: United States Congress
• Parent authority: Federal government
• Jurisdiction: United States
• Collections: U.S. federal laws
• Most recent activity: Defunct
• Most recent activity date: 2025-01-03

Risk subdomain coverage

Good coverage — Comprehensive governance measures or mitigations with clear procedures relating to the risk subdomain are explicitly described.

• Governance failure

Minimal coverage — Brief mention but no specific mitigations, recommendations or governance measures relating to the risk subdomain are described.

• Unequal performance across groups
• Compromise of privacy
• AI system security vulnerabilities
• Lack of capability or robustness
• Lack of transparency or interpretability

All other risk subdomains had no coverage in this document.

Risk Coverage Summary

The document has minimal coverage of AI risk domains, with limited focus on privacy and security (2.1, 2.2), governance structures (6.5), and system safety (7.3, 7.4). The document primarily addresses data governance processes and institutional structures rather than specific AI risks and harms. Coverage is concentrated in governance mechanisms and data quality, with brief mentions of privacy risks and transparency.

Sector coverage

Good coverage — Comprehensive governance measures or mitigations with clear procedures relating to the sector are explicitly described.

• Public Administration (excl. National Security)

All other sectors had no coverage in this document.

Sectors Summary

This document governs AI and data practices across all federal government agencies, which primarily operate in the Public Administration sector. The governance framework applies to federal agencies' internal operations and their use of AI systems across various government functions, but does not extend to private sector entities or specific economic sectors outside of government.

Legislative Status

Hard Law

Legislative Status Summary

This is a binding federal statute enacted by the United States Congress with mandatory obligations on federal agencies, enforceable through congressional oversight and administrative mechanisms.

Actors

Proposer

• Entity Types: AI_Governance_Actor
• Summary: The document is a bill enacted by the United States Congress, as indicated by the standard legislative enactment clause and the legislative process described.

Target

• Entity Types: AI_Governance_Actor
• Summary: The Act targets federal agencies and their leadership positions responsible for data governance and AI adoption, requiring them to establish governance structures, submit reports, and implement best practices for AI and data management.

Enforcer

• Entity Types: AI_Governance_Actor
• Summary: The Director of OMB has authority to issue guidance to agencies based on Council reports. Congressional committees receive mandatory reports for oversight purposes. The Comptroller General conducts biennial evaluations of the Council's effectiveness.

Monitor

• Entity Types: AI_Governance_Actor
• Summary: The Chief Data Officer Council is responsible for biennial reporting on its work and recommendations. The Comptroller General conducts evaluations every 2 years on whether the Council improved use of evidence and program evaluation. Congressional committees receive reports for oversight monitoring.

AI Lifecycle Stages

Lifecycle stages covered by the documents, as defined in the OECD Framework for the Classification of AI Systems

• Plan and Design
• Collect and Process Data
• Build and Use Model
• Verify and Validate
• Deploy
• Operate and Monitor

AI Lifecycle Summary

The document addresses multiple AI lifecycle stages with primary focus on Plan and Design, Build and Use Model, and Operate and Monitor stages. It emphasizes data governance throughout the AI lifecycle from acquisition to disposal, with particular attention to training data quality, testing procedures, and operational monitoring of AI systems.

Technical Scope

Terms relating to technical scope that are mentioned or implicitly referenced in the document

• AI Systems

Technical Scope Summary

The document explicitly mentions artificial intelligence and AI systems throughout, with detailed definitions provided. It does not specifically mention frontier AI, general purpose AI, task-specific AI, foundation models, generative AI, predictive AI, or compute thresholds. It does address synthetic data extensively and briefly mentions data sharing considerations but does not explicitly discuss open-weight or open-source models.